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Found 12 papers, 1 papers with code
Miti-DETR: Object Detection based on Transformers with Mitigatory Self-Attention Convergence
Object Detection with Transformers (DETR) and related works reach or even surpass the highly-optimized Faster-RCNN baseline with self-attention network architectures.
Semantic Clustering based Deduction Learning for Image Recognition and Classification
The paper proposes a semantic clustering based deduction learning by mimicking the learning and thinking process of human brains.
Six-channel Image Representation for Cross-domain Object Detection
If we train the detector using the data from one domain, it cannot perform well on the data from another domain due to domain shift, which is one of the big challenges of most object detection models.
Why Layer-Wise Learning is Hard to Scale-up and a Possible Solution via Accelerated Downsampling
This paper, for the first time, reveals the fundamental reason that impedes the scale-up of layer-wise learning is due to the relatively poor separability of the feature space in shallow layers.
Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting
The state-of-the-art patch rescaling module (PRM) based approaches prove to be very effective in improving the crowd counting performance.
Location-Aware Box Reasoning for Anchor-Based Single-Shot Object Detection
In this paper, we aim at single-shot object detectors and propose a location-aware anchor-based reasoning (LAAR) for the bounding boxes.
Self-Orthogonality Module: A Network Architecture Plug-in for Learning Orthogonal Filters
In this paper, we investigate the empirical impact of orthogonality regularization (OR) in deep learning, either solo or collaboratively.
MDFN: Multi-Scale Deep Feature Learning Network for Object Detection
Compared with features produced in earlier layers, the deep features are better at expressing semantic and contextual information.
Object Detection with Convolutional Neural Networks
In this chapter, we present a brief overview of the recent development in object detection using convolutional neural networks (CNN).
Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization
In this paper, we address the problem of weakly supervisedobject localization (WSL), which trains a detection network on the datasetwith only image-level annotations.
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization
In this paper, we address the problem of weakly supervised object localization (WSL), which trains a detection network on the dataset with only image-level annotations.
MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection
To better handle these challenges, the paper proposes a novel framework, multi-scale, deep inception convolutional neural network (MDCN), which focuses on wider and broader object regions by activating feature maps produced in the deep part of the network.
